Restoring private autism dataset from sanitized database using an optimized key produced from enhanced combined PSO-GWO framework

The timely identification of autism spectrum disorder (ASD) in children is imperative to prevent potential challenges as they grow. When sharing data related to autism for an accurate diagnosis, safeguarding its security and privacy is a paramount concern to fend off unauthorized access, modification, or theft during transmission. Researchers have devised diverse security and privacy models or frameworks, most of which often leverage proprietary algorithms or adapt existing ones to address data leakage. However, conventional anonymization methods, although effective in the sanitization process, proved inadequate for the restoration process. Furthermore, despite numerous scholarly contributions aimed at refining the restoration process, the accuracy of restoration remains notably deficient. Based on the problems identified above, this paper presents a novel approach to data restoration for sanitized sensitive autism datasets with improved performance. In the prior study, we constructed an optimal key for the sanitization process utilizing the proposed Enhanced Combined PSO-GWO framework. This key was implemented to conceal sensitive autism data in the database, thus avoiding information leakage. In this research, the same key was employed during the data restoration process to enhance the accuracy of the original data recovery. Therefore, the study enhanced the restoration process for ASD data's security and privacy by utilizing an optimal key produced via the Enhanced Combined PSO-GWO framework. When compared to existing meta-heuristic algorithms, the simulation results from the autism data restoration experiments demonstrated highly competitive accuracies with 99.90%, 99.60%, 99.50%, 99.25%, and 99.70%, respectively. Among the four types of datasets used, this method outperforms other existing methods on the 30-month autism children dataset, mostly.

A reseaerch work developed a novel Anonymous Authentication (AA) protocol for the Internet of Medical Things (IoMT) that utilized light-weight Elliptic Curve Cryptography 9 .Their work addressed security issues that were identified as a significant vulnerability in a previous AA scheme proposed by another research work 10 .Another study conducted an analysis of the security measures, technologies, and management frameworks related to cloud computing, specifically, in the healthcare domain 11 .The study examined a range of methods to safeguard medical data and identified both challenges and potential solutions using both established and novel techniques.Additionally, it proposed several models that could be employed to address the identified issues in order to achieve optimal outcomes.
A privacy-preserving data mining technique was introduced 12 , where authors enhanced an algorithm, namely the Opposition Intensity-based Cuckoo Search Algorithm (CSA), by modifying the Cuckoo Search Algorithm.In their proposed technique, they formed an appropriate key for data restoration, but there was no consideration of the mechanism of key management protocols, so this is an issue of considering the key management techniques that may hamper the proper restoration of sensitive data.Additionally, a method for distributed clustering was introduced that involved the transfer of sanitized data to a cloud service provider by a helper user in the study of 13 .The effectiveness of this approach was evaluated based on three metrics: transmission time, processing time, and clustering accuracy.Yet, again, a model was proposed, which utilized Artificial Bee Colony (ABC) optimization algorithm to sanitize sensitive information in their proposed model 14 .Another sanitization method called Improved Maximum Sensitive Itemsets Conflict First (IMSICF) algorithm was proposed for privacypreserving utility mining (PPUM) 15 .In this method, maximum conflicts in victim itemsets from sensitive itemsets are tallied to be concealed.This approach chooses transactions with a smaller number of non-sensitive itemsets and a high utility of concealed sensitive itemsets for modification in order to minimize the side effects on non-sensitive information.
The authors in 16 utilized the two optimization algorithms to form a framework called an enhanced combined PSO-GWO meta-heuristic algorithm framework.This framework generated an optimal key for hiding sensitive ASD datasets as an improved method of sanitization process.There was no consideration of data restoration.The authors suggested the need for restoring sensitive data for the security and privacy of medical data after improving their proposed data sanitization process.They mentioned that the same key was promising to restore the sensitive data.The study of 17 discussed the procedures for yielding an optimal key that was employed in the sanitization process as well as the restoration process, but there were shortcomings for taking a longer time to update the key and no fixation of the number of key ranges.The same concern should be addressed for the study of 18,19 , where the outputs were lacking in optimization 18 and ineffectiveness to preserve other sensitive information, such as frequent items 19 .Another approach, namely the Bee-Foraging Learning-based Particle Swarm Optimization (BFL-PSO) algorithm, was introduced to yield the optimal key for data sanitization and restoration 20 .The study considered multi-objective functions, such as error rate, computational time, complexity, etc., to measure its performances, but the performances should be more promising.The same authors in 21 proposed a secured algorithm where they utilized Harmonic Encryption (HE) to fend off attacks for the privacy of sensitive data regarding different diseases like heart disease, diabetes, and cancer.Nonetheless, it needed protocols for time management regarding the analysis of encryption time and decryption time.A further research was conducted on the privacy of electrical health records in the cloud 22 .Mainly, the author developed an approach, namely Elephant Herding Optimization with Opposition-based Learning (EHO-OBL), where they applied blockchain technology to ensure authentication for data integrity.However, the key generation time by the EHO-OBL approach might be enhanced, which was used for data integrity.Moreover, the K-anonymity algorithm was utilized in another study to secure health data privacy 23 .The shortcomings of this approach are that it relies on specific healthcare data, and its reliability or performance is not optimized as well.
Finally, Table 1 provides a brief overview of various recent studies that have implemented advanced techniques or algorithms for data privacy, along with their identified characteristics and challenges.

Methodology
The purpose of this investigation was to identify a viable fix or solution for a problem.In light of this, the issue that needed to be solved was how the restoration process worked appropriately and how to generate optimal keys utilizing the features of meta-heuristic algorithms.To find the best solution, the study analyzed a number of state-of-the-art approaches to the issues.As such, this investigation has determined a study gap concerning the restoration process and the optimal key formation in those cutting-edge solutions.The introduction part highlighted some noteworthy concerns about security and privacy, for which there is yet no clear technological answer in those cutting-edge technologies.Thus, by correctly creating the optimal key and restoration process, the research was able to address these important concerns.
In order to provide the clarification of the issue, this section talks over the main architecture of the proposed framework and its entire working procedures regarding data restoration.

Main architecture
The proposed approach's general architecture is depicted in Fig. 1, with the goal of maintaining high prediction accuracy while protecting the confidentiality and privacy of autism data.The main components of the recommended framework, for instance, are the autism dataset as original data, support vector machine (SVM), processed database, sanitization process, restoration process, sanitization key, PSO, and GWO algorithms.The original database is a raw database that is not prepared to be utilized, because some values can be missing, irregular, irrelevant, or null.So, the original data needs to be pre-processed.Support Vector Machine is employed on the original datasets in order to get the processed database.Using SVM, the datasets have been transformed into an understandable format.It manages the missing and duplicate values.It presents all the information, such as the There are two optimization algorithms, such as particle swarm optimization (PSO) and grey wolf optimization (GWO), which are employed in this recommended framework.In the framework, the characteristics of PSO have been incorporated into GWO to enhance the capability of convergence as well as local searching ability, whose main purpose is to yield the best key for data sanitization and data restoration.The sanitization process is a process where processed data is hidden by the presence of the sanitization key.Different techniques, such as reconstruction of the key matrix, the Khatri-Rao process, binarization, the XOR operation, and so on, have been applied in the process.However, a sanitized database is one that is secured, protected.This sanitized database can also be utilized to archive the original data in the presence of a sanitization key through restoration process.
The restoration process is a technique where an authorised person can get the processed data again in the presence of the sanitized database and sanitization key.The restoration phase is denoted by the blue arrow, while the sanitization phase is denoted by the dark orange arrow.Sensitive autism data is preserved using data restoration process.In this paper, the authors analyse the restoration process depicted in the broken circle and put emphasis on the issue of autism data privacy.From the overall architecture, the restoration process consists of the following components: 1. Processed database 2. Sanitization key 3. Sanitized database 4. Restoration process, and 5. Optimization algorithms.
The interactions among the components of the restoration process are depicted in Fig. 2. The restoration process, including the decoding process and a restoration algorithm, is illustrated in the following subsections broadly.
The symbols used in this section are illustrated as below:

Data restoration
Figure 3 depicts the decoding procedure for data restoration.From the sanitization process and key generation process, the sanitized database, D' and pruned key matrix, K 2 are obtained that are revealed in Eq. ( 1) 16 , However, in this decoding procedure, D′ and K 2 must be binarized.From binarization block, the sanitized database is reduced by considering unit value of input step size.In the interim, the XOR task is performed on the minimized sanitization database and the binarized key matrix, and consequently the restored database is recaptured.
Furthermore, it is noted earlier that key generation procedure yields sanitized key, which is employed to restore database D. This sanitized key is used to generate sanitization database D' from where restored database is achieved by using Eq.(2) below: where D implies restored data and K 2 is the sanitizing key matrix generated from K. Algorithm 1 demonstrates the pseudo code of the whole restoration procedure as below: (1) Output: (the restored database). Procedure:

2:
Binarization of K2 and D .3: Deduction by unit step input from D .

5:
Return Algorithm 1. Pseudo code for restoration process.
To sum up, from the restoration process algorithm, the D' attained from the sanitization procedure and K 2 from the key generation procedure must be binarized.The binarized database from the binarization procedure is deducted from the unit step input.In the interim, the database being subtracted and the binarized key matrix executes a xor operation, and thus the restored database, D , is produced.

Evaluation of objective functions
The three objective functions, such as C 1 , C 2 , as well as C 3 (C 1 refers to the hiding failure rate, C 2 represents the information preservation rate, and C 3 denotes the degree of modification), are evaluated by Eq. ( 3) through Eq. ( 6) 16 .Following the generation of association rules from original database and sanitized database, as well as sensitive rules, this evaluation is carried out.wherein, f s indicates the frequency of sensitive items regarding sanitized data, f m implies the frequency of sensitive items in the matter of original data, f ns denotes the non-sensitive items frequency on the subject of sanitized data, C 3 is the Euclidean distance, additionally, the original data, D, and the sanitised data, D', together yield the C 3 , C 4 represents the distance between each item in the collection of sanitised and original data, w 1 , w 2 , w 3 show a certain cost function affecting C 1 , C 2, and C 3 simultaneously, eventually, f specifies the fitness function of the suggested technique.
However, the objective functions C 1 , C 2, and C 3 are chosen to find out how well the autism data is disinfected by applying the suggested Enhanced Combined PSO-GWO framework.Consequently, the objective functions of the recommended approach are expressed by utilizing the following Eq.( 7) for the medical datasets as below 16 :

Working procedure of the proposed framework with PSO & GWO optimization algorithms
The three vectors in the Particle Swarm Optimisation (PSO) algorithm are called x-, p-, and v-vectors.x-vector tracks the particle's current position in the search area, p-vector (pbest) indicates the position where the particle has found the best solution as possible, and v-vector includes particle velocity, which indicates the future locations of each other particle during the iteration.The first shifting of particles in predetermined directions is done at random.The particle started to migrate in the direction of the previous optimal position on its own since its orientation could be changed gradually.Next, it searches the neighbourhood for the ideal locations to perform certain fitness functions, using the formula, fit = S m − S. In this instance, the particle's position is given as − → M∈ S m , while its velocity is specified as − → w .These two variables are first chosen at random and thereafter updated often in accordance with the two formulas, which are displayed in the following Eqs.( 8) and ( 9) as below 16 : Here, an inertia weight, ω, is an example of a user-defined behavioural parameter that controls the amount of particle velocity recurrence.Both particle's prior best position (pbest position) and its prior best position within the swarm (gbest position) are specified as − → q and − → f , respectively.The particles interact with one another implicitly in this way.Furthermore, r 1 , r 2 ∼ U (0, 1) are used as stochastic variables to weight this, and c 2 , c 1 stand for acceleration constants.Velocity is introduced to the present location so that particle can move towards next location in the search region, irrespective of fitness upgrades that is depicted above in Eq. ( 9).
Whereas, there exists a hierarchy of search agents, for instance, level 1 (Alpha), level 2 (Beta), level 3 (Delta), as well as level 4 (Omega) for the Grey Wolf Optimization (GWO) technique.At the time of hunting their prey, the criteria for encircling the prey are stated mathematically through the Eq. ( 10) to Eq. ( 11) as follows 16 : At this point, the current iteration is denoted by u as well as the coefficient vectors are represented by − → E , − → H .Moreover, the heightened understanding of likely prey sites possessed by alpha, beta, and delta wolves is utilized to computationally replicate the hunting behaviours of grey wolves.No matter whether the remaining amount is needed, the initial best three solutions are considered.The mathematical modelling of the best three solutions is provided in Eqs. ( 12), (13), and ( 14), respectively 16 .
Although conventional algorithms have certain drawbacks, such as reduced performance across multiple domains in the case of the traditional PSO algorithm and limited local search capability, slower convergence, and lower solution accuracy for the GWO algorithm, there is room for improvement.To enhance their effectiveness and integration, further investigation is necessary.As an attempt, this study implements a novel hybrid algorithm to solve these issues highlighted above.In the anticipated Enhanced Combined PSO-GWO, the characteristics of the PSO algorithm are employed into the GWO optimization procedure.The Eqs. (10) and (11) present mathematical model for the prey enclosure in the proposed approach, while Eqs.( 12), (13), and ( 14) illustrate the mathematical model for the hunting method.The primary reformation in the proposed paradigm is the update of the location.Thus, making update for the location in Enhanced Combined PSO-GWO framework is represented in Eq. ( 15), wherein − → M denotes the velocity to update the location of PSO as revealed in Eqs. ( 8) to (9).
Furthermore, the acceleration constants c 1 and c 2 in the conventional PSO regarded as constants, but in the suggested Enhanced Combined PSO-GWO framework, c 1 , c 2 change in accordance with the values 0.1, 0.3, 0.5, 0.7, and 1.0.

System configuration and simulations
This section explains how the proposed technique for restoring autism data works.The various types of datasets with source are discussed in "Dataset description"."Simulation setup" reveals the simulation setup, whereas in "Results and discussions", the paper also demonstrated how the proposed technique performed in simulations when the acceleration constants, c 1 and c 2 , were varied.

Dataset description
The obtained datasets regarding autism from Faculty of Education at Universiti Kebangsaan Malaysia are utilized for this study 39 .The datasets utilized in the experiments encompassed autistic children of various ages, such Vol.:(0123456789)

Simulation setup
The implementation of the recommended methodology was carried out utilizing Python programming platform.The above datasets were employed for this experiments.However, the performances of the proposed framework were measured by some objective functions as parameters, they were the information hiding failure rate (C 1 ), the information preservation rate (C 2 ), the degree of modification rate (C 3 ), and the fitness function (f).Furthermore, the performances were measured by those parameters by setting the different values of the acceleration constants (c 1, c 2 ), where 0 < (c 1, c 2 ) < = 1.

Results and discussions
"Results and discussions"(a) illustrated the achieved performances of the proposed Enhanced Combined PSO-GWO restoration process versus the other traditional algorithms against attacks, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Crow Search Algorithm (CSA), and Adaptive Awareness Probability-based CSA (AAP-CSA).Following that, the effectiveness of this framework was demonstrated by varying the acceleration constants c 1 and c 2 in "Results and discussions"(b).

Comparison with traditional algorithms
Tables 4, 5, 6, and 7 demonstrate the performance analysis by restoration procedure of the Enhanced Combined PSO-GWO framework for the four autism datasets.
Initially, for a 24-month autism child dataset, the proposed algorithm performs 99.26%, 99.43%, 99.32%, 93.14%, and 97.80% better than PSO, DE, GA, AAP-CSA, and CSA for C 1 as shown in Table 4.This model also outperforms GA for C 3 by 72.24%.Furthermore, the recommended approach outperforms AAP-CSA by 43.60% for f.
In summary, the restoration process of the proposed model outperforms other traditional algorithms, as evidenced by the tabular results presented above.

Effectiveness of enhanced combined PSO-GWO based on acceleration constants c 1 and c 2
The restoration of autism data has been measured based on objective functions.Here, the acceleration constants have been updated by varying the values their values between 0 and 1 to satisfy the condition, 0 < (c 1, c 2 ) < = 1.Figures 4, 5, 6, 7 and 8 graphically illustrate the results of performance analysis on cost functions obtained by Eq. ( 8) for four types of autism child datasets based on these varying values.i) The acceleration constants, c 1 = 0.1 and c 2 = 0.1 At first, by taking the 24 months autism child dataset with the values for c 1 = 0.1 and c 2 = 0.1, the outcomes of GA for objective functions C 1 , C 2 , C 3 , and f are 7.333, 0.985, 2199.030, and 3.906, while the proposed technique achieves 0.049, 1.007, 590.40, and 32.80, correspondingly, as shown in Table 8.And the simulation is shown in Fig. 4a, where the proposed technique is 99.33%, 73.15% more improved than GA for C 1 , and C 3 .
ii) The acceleration constants, c 1 = 0.3 and c 2 = 0.3 After that, the values of the acceleration constants are set to c 1 = 0.3 and c 2 = 0.3, so the objective functions, C 1 , C 2 , C 3 , and f for GA are 7.333, 0.985, 2199.030, and 3.906, while the proposed technique achieves 0.049, 1.007, 500.25, and 30.05, respectively, over the 24 months autism child dataset, shown in Table 12.The simulation results are graphically presented in Fig. 5a, where the proposed technique is 99.33%, or 77.25%, more improved than GA for C 1 and C 3 .
Using a 30 months autism child dataset, the results of C  21.The simulation is illustrated in Fig. 7b.So, the recommended technique is 99.89% and 9.53% greater than PSO, 99.81%, and 60.24% superior to GA for C 1 and C 3 , and 99.54%, 27.47%, and 61.09% better than DE, 99.76%, 9.98%, and 53.33% more improved over AAP-CSA for C 1 , C 3 , and f, as well as 99.37%, 48.58%, and 39.12% greater than CSA for C 1 , C 2 , and C 3 , respectively.
Moreover, when considering the values of acceleration constants as c 1 = 1 and c 2 = 1 and using a dataset of autism children spanning over a period of 24 months, the GA yields results of 7.333, 0.985, 2199.030, and 3.906 for C 1 , C 2 , C 3 , and f, respectively.However, the proposed framework produces outcomes of 0.049, 1.007, 610.503, and 33.899 for the same parameters, as depicted in Table 24.Notably, the recommended framework shows a 99.33% and 72.24% improvement over GA for C 1 and C 3 , respectively.The simulation results are illustrated in Fig. 8a.
For the 30 months autism child dataset, the values of objective functions C 1 , C 2 , C 3 , and f for GA are 2.575, 0.999, 2390.050, and 22.434, whereas CSA is 0.791, 2.005, 1560.998, and 4.521, and the proposed method is

Discussions
The proposed restoration process was used to restore the data, and its performance was compared to the existing techniques.The results, which are discussed in "Results and discussions"(a), show that the proposed technique outperforms the existing techniques.Subsequently, the performance of the proposed technique was further improved by varying the acceleration constants within the range of 0 < (c 1 , c 2 ) < = 1, as shown in "Results and discussions"(b).
Therefore, this research work has some significant implications for different arena.Nowadays, healthcare organisations are becoming increasingly concerned about the possibility of personal health data breaches as a result of the quick proliferation of digital health technology.Healthcare data is extremely vulnerable because of cyber hackers.This data is increasingly being hacked these days because hackers' goal is to misuse personal data, which is a profitable financial business for them.Names, dates of birth, health insurance numbers, diagnostic codes, billing information, etc. are among the medical data available to sell for business purposes.Fraudsters use this information to open bank accounts, get passports, or construct forged identification cards in order to manufacture medical equipment or medications.Additionally, according to specialists who have researched cyber-attacks on healthcare institutions, hackers combine a patient number with a bogus provider number, create a file, and then submit a claim with the insurance companies.Since hospitals or clinics have interconnection, easily accessible access points, outdated systems, and a lack of emphasis on cybersecurity for sharing data, hackers may easily obtain enormous amounts of data.Hence, the suggested strategy will be more beneficial for any medical centre or hospital in terms of protecting the privacy of sensitive data, as electronic health records are shared among the physicians, patients, staff, and others, even though other existing approaches show promise.Moreover, future researchers must adhere to this privacy approach in order to safeguard patient confidentiality, cultivate participant confidence, and stop data breaches more appropriately in their further research.
However, it is stated that the study takes into account the analysis through a few objective functions, such as the information hiding failure rate, the information preservation rate, the degree of modification rate, and the fitness function.However, it might potentially function based on decryption time and convergence analysis as well.

Conclusion
In this work, a privacy preservation technique has been developed in the Enhanced Combined PSO-GWO framework.For this purpose, a sanitized key is utilized, which is yielded from the proposed model.This model introduced a restoration process where the optimal key is applied.Therefore, the most important objective of this study is to introduce a restoration technique where the optimal key is used to recover original information securely by permitted users.Moreover, the performances achieved by the recommended model have been compared with the traditional algorithms and attained the expected outcomes.From the experiments and simulation results, the suggested model was assessed in comparison with the different kind of attacks and the enhanced results were achieved.It is stated that this study applied four types of autism datasets in this regard.For the 24 months autism child dataset, the model showed that it achieved 99.27%, 99.33%, 99.43%, 97.83%, and 93.22% greater than PSO, GA, DE, CSA, as well as AAP-CSA, correspondingly.Similarly, the performance analysis of the recommended framework for 30 months autism child dataset reveals that 99.89%, 99.81%,
number of attributes, instances, data types, number of missing values, maximum and minimum values, error values, etc., easily and quickly.If any anomaly occurs in the datasets, it can be solved easily.Now, the processed database is ready to use because it has already been transformed into an understandable and desired format with relevant data, not null or missing values.By getting the processed datasets, it may be sanitized through the sanitization process by providing sanitization key produced by the enhanced combined PSO-GWO approach.
Figure 1.Overall architecture for data security and privacy model 16 .Vol.:(0123456789) Scientific Reports | (2024) 14:15763 | https://doi.org/10.1038/s41598-024-66603-ywww.nature.com/scientificreports/total dataset of autistic children at 24 months comprising of 209 instances along with 26 attributes, at 30 months 209 instances and 29 attributes, at 36 months 234 instances and 31 attributes, and at 48 months featuring 302 instances, 33 attributes.All the datasets were categorized as diagnostic data of autism, with scoring choices: z = 0, v = 5, and x = 10.The cut-off values for each dataset differed, with values of 71, 95, 100, and 105, respectively.Datasets were validated by 8 experts in early childhood development measuring behaviour, self-control, compliance, communication, self-adjustment, autonomy and interaction.Moreover, the data are standard by maintaining some important criteria.The methodology for collecting data is shown in the Table2.Every data type has similar content and format, so they have internal consistency, and concurrent validity.Table3reveals the details of above mentioned four types of datasets.

Table 2 .
Methodology of data collection.

Table 4 .
Analysis of data restoration performance for dataset of 24 months autism child.GA, PSO, CSA, DE, and AAP-CSA algorithms respectively, for C 1 .The framework also attains 48.56% greater than CSA for C 2 .Furthermore, regarding C 3 , the proposed model outperforms GA and CSA by 41% and 9.66%.Table6illustrates the performance improvements of the proposed model when applied to a 36-month autism child dataset in terms of C 1 , which is 11.62%, 90.07%, 11.70%, 86.76%, and 96.47% superior to GA, PSO, CSA, DE, and AAP-CSA algorithms individually.Again, this model outperforms PSO, GA, DE, and CSA by 48.98%, 51.39%, 51.67%, 51.74%, respectively for C 2 , whereas GA by 8.74% for C 3 .Following that, the model also outperforms PSO, DE, and AAP-CSA algorithms by 29.74%, 73.68%, and 41.53% respectively, in the case of f.

Table 5 .
Analysis of data restoration performance for dataset of 30 months autism child.

Table 6 .
Analysis of data restoration performance for 36 months autism child dataset.

Table 7 .
Analysis of data restoration performance for 48 months autism child dataset.

Table 8 .
Cost analysis for 24 months autism data, while c 1 = 0.1 and c 2 = 0.1

Table 9 .
Cost analysis for 30 months autism data, while c 1 = 0.1 and c 2 = 0.1

Table 10 .
Cost analysis for 36 months autism data, while c 1 = 0.1 and c 2 = 0.1

Table 11 .
Cost analysis for 48 months autism data, while c 1 = 0.1 and c 2 = 0.1

Table 12 .
Cost analysis for 24 months autism data, while c 1 = 0.3 and c 2 = 0.3

Table 13 .
Cost analysis for 30 months autism data, while c 1 = 0.3 and c 2 = 0.3

Table 14 .
Cost analysis for 36 months autism data, while c 1 = 0.3 and c 2 = 0.3

Table 15 .
Cost analysis for 48 months autism data, while c 1 = 0.3 and c 2 = 0.3

Table 16 .
Cost analysis for 24 months autism data, while c 1 = 0.5 and c 2 = 0.5

Table 17 .
Cost analysis for 30 months autism data, while c 1 = 0.5 and c 2 = 0.5

Table 18 .
Cost analysis for 36 months autism data, while c 1 = 0.5 and c 2 = 0.5

Table 19 .
Cost analysis for 48 months autism data, while c 1 = 0.5 and c 2 = 0.5

Table 20 .
Cost analysis for 24 months autism data, while c 1 = 0.7 and c 2 = 0.7

Table 21 .
Cost analysis for 30 months autism data, while c 1 = 0.7 and c 2 = 0.7

Table 22 .
Cost analysis for 36 months autism data, while c 1 = 0.7 and c 2 = 0.7

Table 23 .
Cost analysis for 48 months autism data, while c 1 = 0.7 and c 2 = 0.7